我正在使用 RNN模型做某事。但是有些错误使我感到困惑。我使用了tf.layers.conv2d。据我所知，它将改变输入的尺寸。
conv 的输出：
宽度=（W-F + 2P）/ S + 1高度=（H-F + 2P）/ S + 1
池的输出：
W =（W-F）/ S + 1 H =（H-F）/ S + 1
说，因为我的输入形状是（128,1293），然后是conv2d（29,294,32）。结果形状应该是（100,1000,32）。但是变成了（128,1293,32）。< / p>
在模型的结尾，我使用了softmax。 softmax的输入为（5,2），但结果仍然为（5,2）。它不应该是形状为5的向量吗？
我的代码：
def inference(input_mfcc, train):
with tf.variable_scope('conv1'):
# 128*1293 conv1 29*294*32 ===> 100*1000*32
# 100*1000*32 pool1 4*4 s4====>25*250*32
conv1 = tf.layers.conv2d(inputs=input_mfcc,
filters=32,
kernel_size=[29,294],
padding='SAME',
activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(inputs=conv1,pool_size=[4,4],strides=4)
print("conv1:",conv1.get_shape().as_list())
print("pool1:",pool1.get_shape().as_list())
with tf.variable_scope('conv2'):
# 25*250 conv2 6*51*64 ===> 20*200*64
# 20*200*64 pool1 4*4 s4====> 5*50*64
conv2 = tf.layers.conv2d(inputs=pool1,
filters=64,
kernel_size=[6,51],
padding='SAME',
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2,pool_size=[4,4],strides=4)
print("conv2:",conv2.get_shape().as_list())
print("pool2:",pool2.get_shape().as_list())
with tf.variable_scope('conv3'):
#5*5*64
conv3 = tf.layers.conv2d(inputs=pool2,
filters=64,
kernel_size=[1,46],
padding='SAME',
activation=tf.nn.relu)
print("conv3",conv3.get_shape().as_list())
with tf.variable_scope('fc1'):
pool2_flat = tf.reshape(pool2,[5,-1])
print("pool2_flat",pool2_flat.get_shape().as_list())
fc1 = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
dropout1 = tf.layers.dropout(inputs=fc1, rate=0.4, training=train)
print("dropout1",dropout1.get_shape().as_list())
with tf.variable_scope('logits'):
logits = tf.layers.dense(inputs=dropout1, units=2)
predit = tf.nn.softmax(logits=logits)
print("logits",logits.get_shape().as_list())
print("predit",predit.get_shape().as_list())
return predit
def losses(logits,labels):
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,logits=logits,name='cross_entropy')
cross_entropy_loss = tf.reduce_mean(cross_entropy)
return cross_entropy
def training(loss,learning_rate):
with tf.name_scope("optimizer"):
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
global_step = tf.Variable(0, name="global_step", trainable=False)
train_op = optimizer.minimize(loss, global_step=global_step)
return train_op
def evaluation(logits,labels):
with tf.variable_scope("accuracy"):
correct = tf.nn.in_top_k(logits,labels,1)
correct = tf.cast(correct,tf.float32)
accuracy = tf.reduce_mean(correct)
return accuracy
ckpt="./model/music/model.ckpt"
N_CLASSES = 2
MFCC_ROW = 128
MFCC_COL = 1293
INPUT_NODE = MFCC_ROW * MFCC_COL
BATCH_SIZE = 5
CAPACITY = 20
MAX_STEP = 500
learning_rate = 0.0001
def run_train():
mfcc, label= read_TFRecord()
train_batch,train_labels_batch = tf.train.batch([mfcc,label],batch_size=BATCH_SIZE,num_threads=1,capacity=CAPACITY)
print("train_batch",train_batch.get_shape().as_list())
print("labels_batch",train_labels_batch.get_shape().as_list())
train_logits = inference(train_batch,True)
print(train_logits.get_shape().as_list())
train_loss = losses(train_logits, train_labels_batch)
train_op = training(train_loss,learning_rate)
train_acc = evaluation(train_logits,train_labels_batch)
with tf.Session() as sess:
saver = tf.train.Saver()
init_op = tf.group(tf.local_variables_initializer(),
tf.global_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
try:
for step in range(MAX_STEP):
if coord.should_stop():
break;
_,tra_loss,tra_acc = sess.run([train_op,train_loss,train_acc])
# print some
if step%50==0:
print('Step %d,train loss = %.2f,train occuracy = %.2f%%'%(step,tra_loss,tra_acc))
# 100 save
if step % 100 ==0 or (step +1) == MAX_STEP:
saver.save(sess,ckpt,global_step = step)
except tf.errors.OutOfRangeError:
print('Done training epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
run_train()
ValueError Traceback (most recent call last)
<ipython-input-6-c2bffa4d5f17> in <module>()
----> 1 run_train()
<ipython-input-5-1743ee19f55f> in run_train()
18 train_logits = inference(train_batch,True)
19 print(train_logits.get_shape().as_list())
---> 20 train_loss = losses(train_logits, train_labels_batch)
21 train_op = training(train_loss,learning_rate)
22 train_acc = evaluation(train_logits,train_labels_batch)
<ipython-input-4-a0a7b4ee345d> in losses(logits, labels)
1 def losses(logits,labels):
----> 2 cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,logits=logits,name='cross_entropy')
3 cross_entropy_loss = tf.reduce_mean(cross_entropy)
4 return cross_entropy
5
D:\Anaconda3\envs\tfenv\lib\site-packages\tensorflow\python\ops\nn_ops.py in sparse_softmax_cross_entropy_with_logits(_sentinel, labels, logits, name)
2037 raise ValueError("Rank mismatch: Rank of labels (received %s) should "
2038 "equal rank of logits minus 1 (received %s)." %
-> 2039 (labels_static_shape.ndims, logits.get_shape().ndims))
2040 if (static_shapes_fully_defined and
2041 labels_static_shape != logits.get_shape()[:-1]):
ValueError: Rank mismatch: Rank of labels (received 2) should equal rank of logits minus 1 (received 2).
期望输出：
train_batch [5, 128, 1293, 1]
labels_batch [5, 2]
conv1: [5, 100, 1000, 32]
pool1: [5, 25, 250, 32]
conv2: [5, 20, 200, 64]
pool2: [5, 5, 50, 64]
conv3 [5, 5, 5, 64]
pool2_flat [5, 5*5*64]
dropout1 [5, 1024]
logits [5, 2]
predit [5, ]
train_logits [5, ]
实际输出：
train_batch [5, 128, 1293, 1]
labels_batch [5, 2]
conv1: [5, 128, 1293, 32]
pool1: [5, 32, 323, 32]
conv2: [5, 32, 323, 64]
pool2: [5, 8, 80, 64]
conv3 [5, 8, 80, 64]
pool2_flat [5, 40960]
dropout1 [5, 1024]
logits [5, 2]
predit [5, 2]
train_logits [5, 2]
答案 0 :(得分：0)
我使用了tf.layers.conv2d。据我所知，它将改变输入的尺寸。
不。
您使用padding='SAME'，这意味着“将输出填充到输入形状”。
不是形状
(5,)吗？
不，softmax会分别标准化每个值，不会改变形状。